Overview

Dataset statistics

Number of variables10
Number of observations752
Missing cells388
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.9 KiB
Average record size in memory80.2 B

Variable types

NUM9
BOOL1

Warnings

BloodPressure has 28 (3.7%) missing values Missing
Insulin has 360 (47.9%) missing values Missing
df_index has unique values Unique
Pregnancies has 108 (14.4%) zeros Zeros

Reproduction

Analysis started2020-09-30 18:56:04.641951
Analysis finished2020-09-30 18:56:17.672684
Duration13.03 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct752
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.0146277
Minimum0
Maximum767
Zeros1
Zeros (%)0.1%
Memory size5.9 KiB
2020-09-30T11:56:17.770363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.55
Q1194.75
median385.5
Q3577.25
95-th percentile729.45
Maximum767
Range767
Interquartile range (IQR)382.5

Descriptive statistics

Standard deviation221.5469605
Coefficient of variation (CV)0.575424788
Kurtosis-1.198922796
Mean385.0146277
Median Absolute Deviation (MAD)191.5
Skewness-0.003831991044
Sum289531
Variance49083.05571
MonotocityStrictly increasing
2020-09-30T11:56:17.923572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
76710.1%
 
25310.1%
 
26210.1%
 
26110.1%
 
26010.1%
 
25910.1%
 
25810.1%
 
25710.1%
 
25610.1%
 
25510.1%
 
Other values (742)74298.7%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
76710.1%
 
76610.1%
 
76510.1%
 
76410.1%
 
76310.1%
 

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.85106383
Minimum0
Maximum17
Zeros108
Zeros (%)14.4%
Memory size5.9 KiB
2020-09-30T11:56:18.061502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.375189327
Coefficient of variation (CV)0.8764303778
Kurtosis0.1705680019
Mean3.85106383
Median Absolute Deviation (MAD)2
Skewness0.9072902384
Sum2896
Variance11.39190299
MonotocityNot monotonic
2020-09-30T11:56:18.166769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
113217.6%
 
010814.4%
 
210113.4%
 
3749.8%
 
4689.0%
 
5557.3%
 
6486.4%
 
7445.9%
 
8374.9%
 
9283.7%
 
Other values (7)577.6%
 
ValueCountFrequency (%) 
010814.4%
 
113217.6%
 
210113.4%
 
3749.8%
 
4689.0%
 
ValueCountFrequency (%) 
1710.1%
 
1510.1%
 
1420.3%
 
13101.3%
 
1291.2%
 

Glucose
Real number (ℝ≥0)

Distinct135
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.9414894
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:18.301677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation30.60119806
Coefficient of variation (CV)0.2509498467
Kurtosis-0.2954030665
Mean121.9414894
Median Absolute Deviation (MAD)20
Skewness0.5226732772
Sum91700
Variance936.4333229
MonotocityNot monotonic
2020-09-30T11:56:18.438047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100172.3%
 
99172.3%
 
129141.9%
 
111141.9%
 
106141.9%
 
112131.7%
 
108131.7%
 
95131.7%
 
125131.7%
 
109121.6%
 
Other values (125)61281.4%
 
ValueCountFrequency (%) 
4410.1%
 
5610.1%
 
5720.3%
 
6110.1%
 
6210.1%
 
ValueCountFrequency (%) 
19910.1%
 
19810.1%
 
19740.5%
 
19630.4%
 
19520.3%
 

BloodPressure
Real number (ℝ≥0)

MISSING

Distinct46
Distinct (%)6.4%
Missing28
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean72.40055249
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:18.582374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile91.7
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.37987032
Coefficient of variation (CV)0.1709913792
Kurtosis0.9228827146
Mean72.40055249
Median Absolute Deviation (MAD)8
Skewness0.1376292303
Sum52418
Variance153.2611892
MonotocityNot monotonic
2020-09-30T11:56:18.722069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
70577.6%
 
74516.8%
 
78456.0%
 
72445.9%
 
68435.7%
 
64425.6%
 
80395.2%
 
76395.2%
 
60374.9%
 
62344.5%
 
Other values (36)29339.0%
 
ValueCountFrequency (%) 
2410.1%
 
3020.3%
 
3810.1%
 
4010.1%
 
4440.5%
 
ValueCountFrequency (%) 
12210.1%
 
11410.1%
 
11030.4%
 
10820.3%
 
10630.4%
 

SkinThickness
Real number (ℝ≥0)

Distinct51
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.17228464
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:18.858973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q125
median29.17228464
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.852102582
Coefficient of variation (CV)0.303442212
Kurtosis5.344832133
Mean29.17228464
Median Absolute Deviation (MAD)3.827715356
Skewness0.8162601036
Sum21937.55805
Variance78.35972012
MonotocityNot monotonic
2020-09-30T11:56:18.995466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
29.1722846421829.0%
 
32304.0%
 
30273.6%
 
27233.1%
 
23202.7%
 
33202.7%
 
28202.7%
 
18202.7%
 
31192.5%
 
19182.4%
 
Other values (41)33744.8%
 
ValueCountFrequency (%) 
720.3%
 
820.3%
 
1050.7%
 
1160.8%
 
1270.9%
 
ValueCountFrequency (%) 
9910.1%
 
6310.1%
 
6010.1%
 
5610.1%
 
5420.3%
 

Insulin
Real number (ℝ≥0)

MISSING

Distinct184
Distinct (%)46.9%
Missing360
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean156.0561224
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:19.134751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile42.55
Q176.75
median125.5
Q3190
95-th percentile396.5
Maximum846
Range832
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation118.8416898
Coefficient of variation (CV)0.7615317355
Kurtosis6.356505089
Mean156.0561224
Median Absolute Deviation (MAD)54.5
Skewness2.165116186
Sum61174
Variance14123.34723
MonotocityNot monotonic
2020-09-30T11:56:19.539708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105111.5%
 
14091.2%
 
13091.2%
 
12081.1%
 
9470.9%
 
10070.9%
 
18070.9%
 
11060.8%
 
13560.8%
 
11560.8%
 
Other values (174)31642.0%
 
(Missing)36047.9%
 
ValueCountFrequency (%) 
1410.1%
 
1510.1%
 
1610.1%
 
1820.3%
 
2210.1%
 
ValueCountFrequency (%) 
84610.1%
 
74410.1%
 
68010.1%
 
60010.1%
 
57910.1%
 

BMI
Real number (ℝ≥0)

Distinct246
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.45465426
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:19.674840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.2
Q127.5
median32.3
Q336.6
95-th percentile44.5
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.928926198
Coefficient of variation (CV)0.2134956097
Kurtosis0.8748420787
Mean32.45465426
Median Absolute Deviation (MAD)4.6
Skewness0.5968157768
Sum24405.9
Variance48.01001826
MonotocityNot monotonic
2020-09-30T11:56:19.812772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32121.6%
 
31.2121.6%
 
31.6121.6%
 
32.4101.3%
 
33.3101.3%
 
30.891.2%
 
32.891.2%
 
32.991.2%
 
30.191.2%
 
34.281.1%
 
Other values (236)65286.7%
 
ValueCountFrequency (%) 
18.230.4%
 
18.410.1%
 
19.110.1%
 
19.310.1%
 
19.410.1%
 
ValueCountFrequency (%) 
67.110.1%
 
59.410.1%
 
57.310.1%
 
5510.1%
 
53.210.1%
 

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct511
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4730505319
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:19.946580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.141
Q10.244
median0.377
Q30.6275
95-th percentile1.13105
Maximum2.42
Range2.342
Interquartile range (IQR)0.3835

Descriptive statistics

Standard deviation0.3301080525
Coefficient of variation (CV)0.6978283085
Kurtosis5.592024879
Mean0.4730505319
Median Absolute Deviation (MAD)0.17
Skewness1.9040609
Sum355.734
Variance0.1089713263
MonotocityNot monotonic
2020-09-30T11:56:20.091580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.25860.8%
 
0.25460.8%
 
0.23850.7%
 
0.20750.7%
 
0.25950.7%
 
0.26850.7%
 
0.69240.5%
 
0.26340.5%
 
0.19740.5%
 
0.55140.5%
 
Other values (501)70493.6%
 
ValueCountFrequency (%) 
0.07810.1%
 
0.08410.1%
 
0.08520.3%
 
0.08820.3%
 
0.08910.1%
 
ValueCountFrequency (%) 
2.4210.1%
 
2.32910.1%
 
2.28810.1%
 
2.13710.1%
 
1.89310.1%
 

Age
Real number (ℝ≥0)

Distinct52
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.3125
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-09-30T11:56:20.224094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.70939523
Coefficient of variation (CV)0.3515015455
Kurtosis0.6166577132
Mean33.3125
Median Absolute Deviation (MAD)7
Skewness1.116815734
Sum25051
Variance137.1099368
MonotocityNot monotonic
2020-09-30T11:56:20.362547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22689.0%
 
21597.8%
 
25476.2%
 
24456.0%
 
23385.1%
 
28354.7%
 
26324.3%
 
27324.3%
 
29293.9%
 
31243.2%
 
Other values (42)34345.6%
 
ValueCountFrequency (%) 
21597.8%
 
22689.0%
 
23385.1%
 
24456.0%
 
25476.2%
 
ValueCountFrequency (%) 
8110.1%
 
7210.1%
 
7010.1%
 
6910.1%
 
6810.1%
 

Outcome
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
488 
1
264 
ValueCountFrequency (%) 
048864.9%
 
126435.1%
 
2020-09-30T11:56:20.458436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-09-30T11:56:06.995689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:07.159188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:07.426440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:07.558498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:07.672841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:07.794099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:07.916065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.036681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.163990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.296662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.423546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.541586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.667328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.780156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:08.901345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.016023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.131739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.253310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.383837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.512522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.638495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.773771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:09.892646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.016191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.134605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.253968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.376652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.504240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.626840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:10.750896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.038233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.138231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.239356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.348104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.461777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.576993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.691396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.810942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:11.940608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.075596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.195109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.314060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.426601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.529765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.637123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.756612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.867543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:12.971163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.087214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.192005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.291933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.391427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.495210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.611723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.727110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.837159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:13.951997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.062473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.162631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.274922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.374970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.469000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.570445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.681588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.807991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:14.933689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.057725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.361241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.465665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.576103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.684163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.802012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:15.919249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.044760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.162517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.284937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.391208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.505240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.620580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.725370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:16.849893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-30T11:56:20.535904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-30T11:56:20.724555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-30T11:56:20.909168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-30T11:56:21.083909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-30T11:56:17.097328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:17.332030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:17.477784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-30T11:56:17.564582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
006148.072.035.000000NaN33.60.627501
11185.066.029.000000NaN26.60.351310
228183.064.029.172285NaN23.30.672321
33189.066.023.00000094.028.10.167210
440137.040.035.000000168.043.12.288331
555116.074.029.172285NaN25.60.201300
66378.050.032.00000088.031.00.248261
7710115.0NaN29.172285NaN35.30.134290
882197.070.045.000000543.030.50.158531
9104110.092.029.172285NaN37.60.191300

Last rows

df_indexPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7427581106.076.029.172285NaN37.50.197260
7437596190.092.029.172285NaN35.50.278661
744760288.058.026.00000016.028.40.766220
7457619170.074.031.000000NaN44.00.403431
746762989.062.029.172285NaN22.50.142330
74776310101.076.048.000000180.032.90.171630
7487642122.070.027.000000NaN36.80.340270
7497655121.072.023.000000112.026.20.245300
7507661126.060.029.172285NaN30.10.349471
751767193.070.031.000000NaN30.40.315230